library(DESeq2)
library(bcbioRNASeq)

# Shared R Markdown settings
prepareRNASeqTemplate()
if (file.exists("setup.R")) {
    source("setup.R")
}

# Directory paths
dataDir <- file.path(params$outputDir, "data")
countsDir <- file.path(params$outputDir, "results", "counts")
deDir <- file.path(params$outputDir, "results", "differential_expression")

# Load bcbioRNASeq object
bcbName <- load(params$bcbFile)
bcb <- get(bcbName, inherits = FALSE)

# subset to second prep batch as all tumors were done in second batch
mycolData <- colData(bcb) %>% as.data.frame()
bcb_sub <- bcb_sub[,which(mycolData$sampleclass %in% c("primary_tumor", "lung_notumor"))]
mycolData <- colData(bcb_sub) %>% as.data.frame()
bcb_sub <- bcb_sub[,which(mycolData$prepBatch==2)]
# drop low RIN value
mycolData <- colData(bcb_sub) %>% as.data.frame()
mycolData$numericrin <- as.numeric(as.character(mycolData$rin))
bcb_sub <- bcb_sub[,which(mycolData$numericrin>3)]

bcb <- bcb_sub

bcb_sub_lungnotumor_tumor <- bcb
saveData(bcb_sub_lungnotumor_tumor, dir=dataDir)

Overview

  • Principal Investigator:
  • Researcher:
  • Experiment:

> dds <- bcbio(bcb, "DESeqDataSet")
> design(dds) <- params$design
> dds <- DESeq(dds)
> rld <- rlog(dds)

Alpha level (FDR) cutoffs

Let’s take a look at the number of genes we get with different false discovery rate (FDR) cutoffs. These tests subset P values that have been multiple test corrected using the Benjamini Hochberg (BH) method (Benjamini and Hochberg 1995).

> alphaSummary(dds)
0.1 0.05 0.01 0.001 1e-06
LFC > 0 (up) 4994, 25% 4604, 23% 3884, 19% 3195, 16% 1990, 9.9%
LFC < 0 (down) 5375, 27% 4870, 24% 4088, 20% 3335, 17% 2185, 11%
outliers 64, 0.32% 64, 0.32% 64, 0.32% 64, 0.32% 64, 0.32%
low counts 1926, 9.6% 2312, 11% 2697, 13% 2697, 13% 4238, 21%
cutoff (mean count < 1) (mean count < 1) (mean count < 1) (mean count < 1) (mean count < 4)

Results

> # help("results", "DESeq2")
> # For contrast argument as character vector:
> #   1. Design matrix factor of interest.
> #   2. Numerator for LFC (expt).
> #   3. Denominator for LFC (control).
> resUnshrunken <- results(
+     dds,
+     contrast = params$contrast,
+     alpha = params$alpha)
> 
> # DESeqResults with shrunken log2 fold changes (LFC)
> # help("lfcShrink", "DESeq2")
> # Only `coef` or `contrast` can be specified, not both
> # Use the correct `coef` number to modify from `resultsNames(dds)`
> resShrunken <- lfcShrink(
+     dds = dds,
+     # coef = 2,
+     contrast = params$contrast,
+     res = resUnshrunken)
> 
> # Use shrunken LFC values by default
> res <- resShrunken
> res_lungnotumor_tumor <- res
> saveData(res_lungnotumor_tumor, dir = dataDir)

We performed the analysis using a BH adjusted P value cutoff of 0.05 and a log fold-change (LFC) ratio cutoff of 1.

Plots

Mean average (MA)

An MA plot compares transformed counts on M (log ratio) and A (mean average) scales (Y. H. Yang et al. 2002).

> plotMA(res)

Volcano

A volcano plot compares significance (BH-adjusted P value) against fold change (log2) (Cui and Churchill 2003; Li et al. 2014). Genes in the green box with text labels have an adjusted P value are likely to be the top candidate genes of interest.

> plotVolcano(res, lfc = params$lfc)

Heatmap

This plot shows only differentially expressed genes on a per-sample basis. We have scaled the data by row and used the ward.D2 method for clustering (Ward 1963).

> plotDEGHeatmap(res, counts = rld)

> top50res <- subset(res, padj < 0.05) %>% .[order(.$padj), ] %>% .[1:50, ]
> top50gene <- row.names(top50res)
> 
> plotHeatmap(bcb, top50gene, normalized = "rlog")

File downloads

The results are saved as gzip-compressed comma separated values (CSV). Gzip compression is natively supported on macOS and Linux-based operating systems. If you’re running Windows, we recommend installing 7-Zip. CSV files can be opened in Excel or RStudio.

Count matrices

  • normalizedCounts.csv.gz: Use to evaluate individual genes and/or generate plots. These counts are normalized for the variation in sequencing depth across samples.
  • tpm.csv.gz: Transcripts per million, scaled by length and also suitable for plotting.
  • rawCounts.csv.gz: Only use to perform a new differential expression analysis. These counts will vary across samples due to differences in sequencing depth, and have not been normalized. Do not use this file for plotting genes.

Differentially expressed genes (DEG)

> resTbl <- resultsTables(res, lfc = params$lfc, write = TRUE, summary = TRUE, 
+     headerLevel = 3, dir = deDir)

Summary statistics

  • 25168 genes in count matrix
  • base mean > 0: 20137 genes (non-zero)
  • base mean > 1: 17848 genes
  • alpha cutoff: 0.05
  • lfc cutoff: 1 (applied in tables only)
  • deg pass alpha: 9474 genes
  • deg lfc up: 3039 genes
  • deg lfc down: 3710 genes

DEG tables are sorted by BH-adjusted P value, and contain the following columns:

  • ensgene: Ensembl gene identifier.
  • baseMean: Mean of the normalized counts per gene for all samples.
  • log2FoldChange: log2 fold change.
  • lfcSE: log2 standard error.
  • stat: Wald statistic.
  • pvalue: Walt test P value.
  • padj: BH adjusted Wald test P value (corrected for multiple comparisons; aka FDR).
  • externalGeneName: Ensembl name (a.k.a. symbol).
  • description: Ensembl description.
  • geneBiotype: Ensembl biotype (e.g. protein_coding).

Top tables

Only the top up- and down-regulated genes (arranged by log2 fold change) are shown.

> topTables(resTbl)
sampleclass primary tumor vs lung notumor (upregulated)
ensgene baseMean lfc padj symbol description
ENSMUSG00000026822 4823 5.67 0.00e+00 Lcn2 lipocalin 2
ENSMUSG00000058773 1635 5.57 5.65e-148 Hist1h1b histone cluster 1, H1b
ENSMUSG00000105827 2827 4.29 1.97e-139 Hist2h2bb histone cluster 2, H2bb
ENSMUSG00000017716 1271 6.79 2.28e-139 Birc5 baculoviral IAP repeat-containing 5
ENSMUSG00000041782 520 5.36 1.73e-137 Lad1 ladinin
ENSMUSG00000001622 1568 9.22 1.30e-136 Csn3 casein kappa
ENSMUSG00000029304 8499 4.57 1.52e-133 Spp1 secreted phosphoprotein 1
ENSMUSG00000033256 452 4.27 4.74e-116 Shf Src homology 2 domain containing F
ENSMUSG00000035105 721 7.33 3.66e-113 Egln3 egl-9 family hypoxia-inducible factor 3
ENSMUSG00000000567 406 6.75 4.38e-112 Sox9 SRY (sex determining region Y)-box 9
ENSMUSG00000028645 901 4.34 1.07e-111 Slc2a1 solute carrier family 2 (facilitated glucose transporter), member 1
ENSMUSG00000026185 9195 4.09 3.09e-111 Igfbp5 insulin-like growth factor binding protein 5
ENSMUSG00000059040 1894 3.62 4.41e-111 Eno1b enolase 1B, retrotransposed
ENSMUSG00000038400 1684 3.25 1.06e-109 Pmepa1 prostate transmembrane protein, androgen induced 1
ENSMUSG00000049539 1508 5.75 6.28e-106 Hist1h1a histone cluster 1, H1a
ENSMUSG00000069308 498 4.33 1.29e-102 Hist1h2bp histone cluster 1, H2bp
ENSMUSG00000052565 3121 3.97 8.09e-93 Hist1h1d histone cluster 1, H1d
ENSMUSG00000038679 1843 3.80 9.64e-86 Trps1 transcriptional repressor GATA binding 1
ENSMUSG00000069268 545 4.50 3.02e-85 Hist1h2bf histone cluster 1, H2bf
ENSMUSG00000069266 551 4.02 1.57e-84 Hist1h4b histone cluster 1, H4b
ENSMUSG00000026051 1164 9.50 1.14e-83 1500015O10Rik RIKEN cDNA 1500015O10 gene
ENSMUSG00000024640 749 4.99 4.48e-81 Psat1 phosphoserine aminotransferase 1
ENSMUSG00000075031 1266 3.94 5.16e-80 Hist1h2bb histone cluster 1, H2bb
ENSMUSG00000044303 365 5.01 4.00e-78 Cdkn2a cyclin-dependent kinase inhibitor 2A
ENSMUSG00000017390 713 5.22 4.84e-78 Aldoc aldolase C, fructose-bisphosphate
ENSMUSG00000064288 4484 3.34 1.92e-77 Hist1h4k histone cluster 1, H4k
ENSMUSG00000009185 441 8.17 5.92e-76 Ccl8 chemokine (C-C motif) ligand 8
ENSMUSG00000027469 392 4.74 4.42e-73 Tpx2 TPX2, microtubule-associated
ENSMUSG00000069301 264 4.88 4.72e-73 Hist1h2ag histone cluster 1, H2ag
ENSMUSG00000101972 1524 4.48 5.96e-72 Hist1h3i histone cluster 1, H3i
ENSMUSG00000033006 307 7.47 9.61e-72 Sox10 SRY (sex determining region Y)-box 10
ENSMUSG00000047945 830 3.71 2.08e-71 Marcksl1 MARCKS-like 1
ENSMUSG00000074480 231 5.04 8.12e-71 Mex3a mex3 RNA binding family member A
ENSMUSG00000054342 460 4.29 1.97e-70 Kcnn4 potassium intermediate/small conductance calcium-activated channel, subfamily N, member 4
ENSMUSG00000023885 247 5.22 5.90e-70 Thbs2 thrombospondin 2
ENSMUSG00000070803 350 7.47 1.09e-69 Cited4 Cbp/p300-interacting transactivator, with Glu/Asp-rich carboxy-terminal domain, 4
ENSMUSG00000012428 401 4.32 5.64e-69 Steap4 STEAP family member 4
ENSMUSG00000063021 546 4.60 1.36e-67 Hist1h2ak histone cluster 1, H2ak
ENSMUSG00000022382 215 4.28 1.90e-66 Wnt7b wingless-type MMTV integration site family, member 7B
ENSMUSG00000036777 233 3.86 1.90e-66 Anln anillin, actin binding protein
ENSMUSG00000020737 1939 3.75 3.20e-66 Jpt1 Jupiter microtubule associated homolog 1
ENSMUSG00000041431 201 5.29 5.04e-66 Ccnb1 cyclin B1
ENSMUSG00000062727 916 4.62 3.35e-65 Hist1h2bk histone cluster 1, H2bk
ENSMUSG00000025574 844 3.69 2.44e-64 Tk1 thymidine kinase 1
ENSMUSG00000025747 594 3.99 3.91e-64 Tyms thymidylate synthase
ENSMUSG00000037035 374 3.04 4.95e-64 Inhbb inhibin beta-B
ENSMUSG00000101355 2216 4.74 1.07e-63 Hist1h3h histone cluster 1, H3h
ENSMUSG00000074403 2315 4.55 1.37e-63 Hist2h3b histone cluster 2, H3b
ENSMUSG00000081058 1164 5.19 1.33e-62 Hist2h3c2 histone cluster 2, H3c2
ENSMUSG00000006403 205 6.63 2.90e-62 Adamts4 a disintegrin-like and metallopeptidase (reprolysin type) with thrombospondin type 1 motif, 4
sampleclass primary tumor vs lung notumor (downregulated)
ensgene baseMean lfc padj symbol description
ENSMUSG00000023959 4864 -8.58 0.00e+00 Clic5 chloride intracellular channel 5
ENSMUSG00000029188 7690 -8.72 0.00e+00 Slc34a2 solute carrier family 34 (sodium phosphate), member 2
ENSMUSG00000056492 5611 -5.85 7.50e-272 Adgrf5 adhesion G protein-coupled receptor F5
ENSMUSG00000015452 5684 -8.65 1.23e-260 Ager advanced glycosylation end product-specific receptor
ENSMUSG00000054619 3126 -6.55 1.55e-254 Mettl7a1 methyltransferase like 7A1
ENSMUSG00000024140 12792 -5.89 1.75e-251 Epas1 endothelial PAS domain protein 1
ENSMUSG00000021789 8511 -9.90 5.71e-234 Sftpa1 surfactant associated protein A1
ENSMUSG00000068874 1625 -6.01 2.94e-223 Selenbp1 selenium binding protein 1
ENSMUSG00000025150 10555 -6.65 5.08e-212 Cbr2 carbonyl reductase 2
ENSMUSG00000003477 6041 -10.06 2.60e-210 Inmt indolethylamine N-methyltransferase
ENSMUSG00000053198 1486 -5.91 9.18e-208 Prx periaxin
ENSMUSG00000045954 2272 -4.17 3.05e-191 Cavin2 caveolae associated 2
ENSMUSG00000021057 2868 -8.60 1.70e-184 Akap5 A kinase (PRKA) anchor protein 5
ENSMUSG00000041134 1019 -5.42 3.34e-175 Cyyr1 cysteine and tyrosine-rich protein 1
ENSMUSG00000049690 1202 -4.79 2.10e-174 Nckap5 NCK-associated protein 5
ENSMUSG00000074743 2910 -6.77 3.40e-171 Thbd thrombomodulin
ENSMUSG00000029375 2890 -8.83 2.22e-169 Cxcl15 chemokine (C-X-C motif) ligand 15
ENSMUSG00000014846 1540 -5.89 2.82e-167 Tppp3 tubulin polymerization-promoting protein family member 3
ENSMUSG00000046733 1142 -6.11 2.38e-165 Gprc5a G protein-coupled receptor, family C, group 5, member A
ENSMUSG00000004655 2579 -4.85 2.90e-163 Aqp1 aquaporin 1
ENSMUSG00000028713 1267 -6.78 9.61e-162 Cyp4b1 cytochrome P450, family 4, subfamily b, polypeptide 1
ENSMUSG00000045930 1282 -5.96 7.24e-161 Clec14a C-type lectin domain family 14, member a
ENSMUSG00000032473 3985 -9.93 2.28e-159 Cldn18 claudin 18
ENSMUSG00000033032 1152 -4.93 9.53e-158 Afap1l1 actin filament associated protein 1-like 1
ENSMUSG00000052974 3771 -8.25 3.16e-157 Cyp2f2 cytochrome P450, family 2, subfamily f, polypeptide 2
ENSMUSG00000033960 2890 -4.00 1.12e-153 Jcad junctional cadherin 5 associated
ENSMUSG00000022836 1476 -4.57 3.29e-150 Mylk myosin, light polypeptide kinase
ENSMUSG00000020154 4103 -5.88 1.53e-146 Ptprb protein tyrosine phosphatase, receptor type, B
ENSMUSG00000020315 15138 -3.29 1.64e-145 Sptbn1 spectrin beta, non-erythrocytic 1
ENSMUSG00000041378 1240 -6.37 9.49e-144 Cldn5 claudin 5
ENSMUSG00000000093 913 -6.35 1.10e-143 Tbx2 T-box 2
ENSMUSG00000054986 3630 -10.17 2.15e-140 Sec14l3 SEC14-like lipid binding 3
ENSMUSG00000001240 1114 -5.61 2.39e-140 Ramp2 receptor (calcitonin) activity modifying protein 2
ENSMUSG00000017754 2088 -3.61 2.91e-140 Pltp phospholipid transfer protein
ENSMUSG00000006386 1282 -5.76 9.80e-140 Tek endothelial-specific receptor tyrosine kinase
ENSMUSG00000015354 1275 -5.55 1.21e-139 Pcolce2 procollagen C-endopeptidase enhancer 2
ENSMUSG00000030020 776 -6.21 2.08e-139 Prickle2 prickle planar cell polarity protein 2
ENSMUSG00000030340 1275 -7.62 4.08e-136 Scnn1a sodium channel, nonvoltage-gated 1 alpha
ENSMUSG00000024451 771 -4.44 1.34e-135 Arap3 ArfGAP with RhoGAP domain, ankyrin repeat and PH domain 3
ENSMUSG00000069763 1552 -8.39 2.72e-133 Tmem100 transmembrane protein 100
ENSMUSG00000048960 1789 -5.80 2.82e-133 Prex2 phosphatidylinositol-3,4,5-trisphosphate-dependent Rac exchange factor 2
ENSMUSG00000018339 2143 -4.20 9.28e-132 Gpx3 glutathione peroxidase 3
ENSMUSG00000020681 3749 -4.59 3.87e-131 Ace angiotensin I converting enzyme (peptidyl-dipeptidase A) 1
ENSMUSG00000020044 3960 -4.29 1.03e-130 Timp3 tissue inhibitor of metalloproteinase 3
ENSMUSG00000069515 7947 -7.54 1.72e-130 Lyz1 lysozyme 1
ENSMUSG00000021904 935 -6.85 1.77e-128 Sema3g sema domain, immunoglobulin domain (Ig), short basic domain, secreted, (semaphorin) 3G
ENSMUSG00000001300 1523 -4.24 3.27e-125 Efnb2 ephrin B2
ENSMUSG00000002980 2215 -6.34 8.21e-125 Bcam basal cell adhesion molecule
ENSMUSG00000053279 1924 -9.09 3.36e-122 Aldh1a1 aldehyde dehydrogenase family 1, subfamily A1
ENSMUSG00000042812 1273 -8.18 1.10e-121 Foxf1 forkhead box F1

Expression patterns

> top10res <- subset(res, padj < 0.05) %>% .[order(.$padj), ] %>% .[1:10, ]
> top10gene <- row.names(top10res)
> 
> plots <- plotGene(bcb, top10gene, returnList = TRUE)
> 
> n = 1
> cat(paste("##", plots[[n]]$labels$title))

Clic5

> plots[[n]]

> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))

Lcn2

> plots[[n]]

> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))

Slc34a2

> plots[[n]]

> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))

Adgrf5

> plots[[n]]

> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))

Ager

> plots[[n]]

> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))

Mettl7a1

> plots[[n]]

> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))

Epas1

> plots[[n]]

> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))

Sftpa1

> plots[[n]]

> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))

Selenbp1

> plots[[n]]

> n = n + 1
> cat(paste("##", plots[[n]]$labels$title))

Cbr2

> plots[[n]]


Methods

RNA-seq counts were generated by bcbio and bcbioRNASeq using salmon (Patro et al. 2017). Counts were imported into R using tximport (Soneson, Love, and Robinson 2016) and DESeq2 (Love, Huber, and Anders 2014). Gene annotations were obtained from Ensembl. Plots were generated by ggplot2 (Wickham 2009). Heatmaps were generated by pheatmap (Kolde 2015).

R session information

> mdHeader("`devtools::session_info()`", level = 2)

devtools::session_info()

> devtools::session_info()
##  setting  value                       
##  version  R version 3.4.3 (2017-11-30)
##  system   x86_64, darwin15.6.0        
##  ui       X11                         
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  tz       America/New_York            
##  date     2018-02-05                  
## 
##  package                * version   date      
##  acepack                  1.4.1     2016-10-29
##  affy                     1.56.0    2017-10-31
##  affyio                   1.48.0    2017-10-31
##  annotate                 1.56.1    2017-11-13
##  AnnotationDbi            1.40.0    2017-10-31
##  AnnotationFilter         1.2.0     2017-10-31
##  AnnotationHub            2.10.1    2017-11-08
##  assertthat               0.2.0     2017-04-11
##  backports                1.1.2     2017-12-13
##  base                   * 3.4.3     2017-12-07
##  base64enc                0.1-3     2015-07-28
##  basejump                 0.2.0     2018-01-29
##  bcbioBase              * 0.0.3     2018-01-29
##  bcbioRNASeq            * 0.1.4     2018-01-30
##  bindr                    0.1       2016-11-13
##  bindrcpp               * 0.2       2017-06-17
##  Biobase                * 2.38.0    2017-10-31
##  BiocGenerics           * 0.24.0    2017-10-31
##  BiocInstaller            1.28.0    2017-10-31
##  BiocParallel             1.12.0    2017-10-31
##  biomaRt                  2.34.2    2018-01-20
##  Biostrings               2.46.0    2017-10-31
##  bit                      1.1-12    2014-04-09
##  bit64                    0.9-7     2017-05-08
##  bitops                   1.0-6     2013-08-17
##  blob                     1.1.0     2017-06-17
##  broom                    0.4.3     2017-11-20
##  cellranger               1.1.0     2016-07-27
##  checkmate                1.8.5     2017-10-24
##  circlize                 0.4.3     2017-12-20
##  cli                      1.0.0     2017-11-05
##  cluster                  2.0.6     2017-03-10
##  codetools                0.2-15    2016-10-05
##  colorspace               1.3-2     2016-12-14
##  compiler                 3.4.3     2017-12-07
##  ComplexHeatmap           1.17.1    2017-10-25
##  ConsensusClusterPlus     1.42.0    2017-10-31
##  cowplot                  0.9.2     2017-12-17
##  crayon                   1.3.4     2017-09-16
##  curl                     3.1       2017-12-12
##  data.table               1.10.4-3  2017-10-27
##  datasets               * 3.4.3     2017-12-07
##  DBI                      0.7       2017-06-18
##  DEGreport              * 1.14.1    2017-12-19
##  DelayedArray           * 0.4.1     2017-11-07
##  dendsort                 0.3.3     2015-12-14
##  DESeq2                 * 1.18.1    2017-11-12
##  devtools                 1.13.4    2017-11-09
##  digest                   0.6.15    2018-01-28
##  dplyr                  * 0.7.4     2017-09-28
##  edgeR                    3.20.7    2018-01-18
##  ensembldb                2.2.0     2017-10-31
##  evaluate                 0.10.1    2017-06-24
##  forcats                * 0.2.0     2017-01-23
##  foreign                  0.8-69    2017-06-22
##  formatR                  1.5       2017-04-25
##  Formula                  1.2-2     2017-07-10
##  genefilter               1.60.0    2017-10-31
##  geneplotter              1.56.0    2017-10-31
##  GenomeInfoDb           * 1.14.0    2017-10-31
##  GenomeInfoDbData         1.0.0     2018-01-29
##  GenomicAlignments        1.14.1    2017-11-18
##  GenomicFeatures          1.30.2    2018-01-31
##  GenomicRanges          * 1.30.1    2017-12-21
##  GetoptLong               0.1.6     2017-03-07
##  ggplot2                * 2.2.1     2016-12-30
##  ggrepel                  0.7.0     2017-09-29
##  GlobalOptions            0.0.12    2017-05-21
##  glue                     1.2.0     2017-10-29
##  graphics               * 3.4.3     2017-12-07
##  grDevices              * 3.4.3     2017-12-07
##  grid                     3.4.3     2017-12-07
##  gridExtra                2.3       2017-09-09
##  grr                      0.9.5     2016-08-26
##  gtable                   0.2.0     2016-02-26
##  haven                    1.1.1     2018-01-18
##  highr                    0.6       2016-05-09
##  Hmisc                    4.1-1     2018-01-03
##  hms                      0.4.1     2018-01-24
##  htmlTable                1.11.2    2018-01-20
##  htmltools                0.3.6     2017-04-28
##  htmlwidgets              1.0       2018-01-20
##  httpuv                   1.3.5     2017-07-04
##  httr                     1.3.1     2017-08-20
##  interactiveDisplayBase   1.16.0    2017-10-31
##  IRanges                * 2.12.0    2017-10-31
##  jsonlite                 1.5       2017-06-01
##  knitr                  * 1.19      2018-01-29
##  labeling                 0.3       2014-08-23
##  lattice                  0.20-35   2017-03-25
##  latticeExtra             0.6-28    2016-02-09
##  lazyeval                 0.2.1     2017-10-29
##  limma                    3.34.6    2018-01-24
##  locfit                   1.5-9.1   2013-04-20
##  logging                  0.7-103   2013-04-12
##  lubridate                1.7.1     2017-11-03
##  magrittr                 1.5       2014-11-22
##  Matrix                   1.2-12    2017-11-20
##  Matrix.utils             0.9.6     2017-08-28
##  MatrixModels             0.4-1     2015-08-22
##  matrixStats            * 0.53.0    2018-01-24
##  memoise                  1.1.0     2017-04-21
##  methods                * 3.4.3     2017-12-07
##  mime                     0.5       2016-07-07
##  mnormt                   1.5-5     2016-10-15
##  modelr                   0.1.1     2017-07-24
##  munsell                  0.4.3     2016-02-13
##  nlme                     3.1-131   2017-02-06
##  nnet                     7.3-12    2016-02-02
##  Nozzle.R1                1.1-1     2013-05-15
##  parallel               * 3.4.3     2017-12-07
##  pheatmap                 1.0.8     2015-12-11
##  pillar                   1.1.0     2018-01-14
##  pkgconfig                2.0.1     2017-03-21
##  plyr                     1.8.4     2016-06-08
##  preprocessCore           1.40.0    2017-10-31
##  prettyunits              1.0.2     2015-07-13
##  progress                 1.1.2     2016-12-14
##  ProtGenerics             1.10.0    2017-10-31
##  psych                    1.7.8     2017-09-09
##  purrr                  * 0.2.4     2017-10-18
##  quantreg               * 5.34      2017-10-25
##  R.methodsS3              1.7.1     2016-02-16
##  R.oo                     1.21.0    2016-11-01
##  R.utils                  2.6.0     2017-11-05
##  R6                       2.2.2     2017-06-17
##  RColorBrewer             1.1-2     2014-12-07
##  Rcpp                     0.12.15   2018-01-20
##  RCurl                    1.95-4.10 2018-01-04
##  readr                  * 1.1.1     2017-05-16
##  readxl                   1.0.0     2017-04-18
##  reshape                  0.8.7     2017-08-06
##  reshape2                 1.4.3     2017-12-11
##  rjson                    0.2.15    2014-11-03
##  rlang                    0.1.6     2017-12-21
##  rmarkdown                1.8       2017-11-17
##  RMySQL                   0.10.13   2017-08-14
##  rpart                    4.1-12    2018-01-12
##  rprojroot                1.3-2     2018-01-03
##  Rsamtools                1.30.0    2017-10-31
##  RSQLite                  2.0       2017-06-19
##  rstudioapi               0.7       2017-09-07
##  rtracklayer              1.38.3    2018-01-23
##  rvest                    0.3.2     2016-06-17
##  S4Vectors              * 0.16.0    2017-10-31
##  scales                   0.5.0     2017-08-24
##  shape                    1.4.3     2017-08-16
##  shiny                    1.0.5     2017-08-23
##  SparseM                * 1.77      2017-04-23
##  splines                  3.4.3     2017-12-07
##  stats                  * 3.4.3     2017-12-07
##  stats4                 * 3.4.3     2017-12-07
##  stringi                  1.1.6     2017-11-17
##  stringr                * 1.2.0     2017-02-18
##  SummarizedExperiment   * 1.8.1     2017-12-19
##  survival                 2.41-3    2017-04-04
##  tibble                 * 1.4.2     2018-01-22
##  tidyr                  * 0.8.0     2018-01-29
##  tidyverse              * 1.2.1     2017-11-14
##  tools                    3.4.3     2017-12-07
##  tximport                 1.6.0     2017-10-31
##  utils                  * 3.4.3     2017-12-07
##  viridis                  0.4.1     2018-01-08
##  viridisLite              0.3.0     2018-02-01
##  vsn                      3.46.0    2017-10-31
##  withr                    2.1.1     2017-12-19
##  XML                      3.98-1.9  2017-06-19
##  xml2                     1.2.0     2018-01-24
##  xtable                   1.8-2     2016-02-05
##  XVector                  0.18.0    2017-10-31
##  yaml                     2.1.16    2017-12-12
##  zlibbioc                 1.24.0    2017-10-31
##  source                              
##  cran (@1.4.1)                       
##  cran (@1.56.0)                      
##  cran (@1.48.0)                      
##  Bioconductor                        
##  Bioconductor                        
##  cran (@1.2.0)                       
##  cran (@2.10.1)                      
##  CRAN (R 3.4.0)                      
##  CRAN (R 3.4.3)                      
##  local                               
##  CRAN (R 3.4.0)                      
##  Github (steinbaugh/basejump@265d3ce)
##  Github (hbc/bcbioBase@dc61e83)      
##  Github (hbc/bcbioRNASeq@f44ad74)    
##  CRAN (R 3.4.0)                      
##  CRAN (R 3.4.0)                      
##  Bioconductor                        
##  Bioconductor                        
##  Bioconductor                        
##  cran (@1.12.0)                      
##  cran (@2.34.2)                      
##  cran (@2.46.0)                      
##  CRAN (R 3.4.0)                      
##  CRAN (R 3.4.0)                      
##  CRAN (R 3.4.0)                      
##  CRAN (R 3.4.0)                      
##  CRAN (R 3.4.3)                      
##  CRAN (R 3.4.0)                      
##  cran (@1.8.5)                       
##  cran (@0.4.3)                       
##  CRAN (R 3.4.2)                      
##  CRAN (R 3.4.3)                      
##  CRAN (R 3.4.3)                      
##  CRAN (R 3.4.0)                      
##  local                               
##  cran (@1.17.1)                      
##  cran (@1.42.0)                      
##  cran (@0.9.2)                       
##  CRAN (R 3.4.1)                      
##  CRAN (R 3.4.3)                      
##  cran (@1.10.4-)                     
##  local                               
##  CRAN (R 3.4.0)                      
##  cran (@1.14.1)                      
##  cran (@0.4.1)                       
##  cran (@0.3.3)                       
##  cran (@1.18.1)                      
##  CRAN (R 3.4.2)                      
##  CRAN (R 3.4.3)                      
##  CRAN (R 3.4.2)                      
##  cran (@3.20.7)                      
##  cran (@2.2.0)                       
##  CRAN (R 3.4.1)                      
##  CRAN (R 3.4.0)                      
##  CRAN (R 3.4.3)                      
##  cran (@1.5)                         
##  cran (@1.2-2)                       
##  Bioconductor                        
##  Bioconductor                        
##  cran (@1.14.0)                      
##  Bioconductor                        
##  cran (@1.14.1)                      
##  Bioconductor                        
##  cran (@1.30.1)                      
##  cran (@0.1.6)                       
##  CRAN (R 3.4.0)                      
##  cran (@0.7.0)                       
##  cran (@0.0.12)                      
##  CRAN (R 3.4.2)                      
##  local                               
##  local                               
##  local                               
##  cran (@2.3)                         
##  cran (@0.9.5)                       
##  CRAN (R 3.4.0)                      
##  CRAN (R 3.4.3)                      
##  CRAN (R 3.4.0)                      
##  cran (@4.1-1)                       
##  CRAN (R 3.4.3)                      
##  cran (@1.11.2)                      
##  CRAN (R 3.4.0)                      
##  cran (@1.0)                         
##  cran (@1.3.5)                       
##  CRAN (R 3.4.1)                      
##  cran (@1.16.0)                      
##  Bioconductor                        
##  CRAN (R 3.4.0)                      
##  CRAN (R 3.4.3)                      
##  CRAN (R 3.4.0)                      
##  CRAN (R 3.4.3)                      
##  cran (@0.6-28)                      
##  CRAN (R 3.4.2)                      
##  cran (@3.34.6)                      
##  CRAN (R 3.4.0)                      
##  cran (@0.7-103)                     
##  CRAN (R 3.4.2)                      
##  CRAN (R 3.4.0)                      
##  CRAN (R 3.4.3)                      
##  cran (@0.9.6)                       
##  cran (@0.4-1)                       
##  cran (@0.53.0)                      
##  CRAN (R 3.4.0)                      
##  local                               
##  CRAN (R 3.4.0)                      
##  CRAN (R 3.4.0)                      
##  CRAN (R 3.4.1)                      
##  CRAN (R 3.4.0)                      
##  CRAN (R 3.4.3)                      
##  CRAN (R 3.4.3)                      
##  cran (@1.1-1)                       
##  local                               
##  cran (@1.0.8)                       
##  CRAN (R 3.4.3)                      
##  CRAN (R 3.4.0)                      
##  CRAN (R 3.4.0)                      
##  cran (@1.40.0)                      
##  cran (@1.0.2)                       
##  cran (@1.1.2)                       
##  cran (@1.10.0)                      
##  CRAN (R 3.4.3)                      
##  CRAN (R 3.4.2)                      
##  cran (@5.34)                        
##  cran (@1.7.1)                       
##  cran (@1.21.0)                      
##  cran (@2.6.0)                       
##  CRAN (R 3.4.0)                      
##  CRAN (R 3.4.0)                      
##  CRAN (R 3.4.3)                      
##  CRAN (R 3.4.3)                      
##  CRAN (R 3.4.0)                      
##  CRAN (R 3.4.0)                      
##  cran (@0.8.7)                       
##  CRAN (R 3.4.3)                      
##  cran (@0.2.15)                      
##  CRAN (R 3.4.3)                      
##  CRAN (R 3.4.2)                      
##  cran (@0.10.13)                     
##  CRAN (R 3.4.3)                      
##  CRAN (R 3.4.3)                      
##  cran (@1.30.0)                      
##  CRAN (R 3.4.1)                      
##  CRAN (R 3.4.1)                      
##  cran (@1.38.3)                      
##  CRAN (R 3.4.0)                      
##  Bioconductor                        
##  CRAN (R 3.4.1)                      
##  cran (@1.4.3)                       
##  cran (@1.0.5)                       
##  cran (@1.77)                        
##  local                               
##  local                               
##  local                               
##  CRAN (R 3.4.2)                      
##  CRAN (R 3.4.0)                      
##  cran (@1.8.1)                       
##  CRAN (R 3.4.3)                      
##  CRAN (R 3.4.3)                      
##  CRAN (R 3.4.3)                      
##  CRAN (R 3.4.2)                      
##  local                               
##  cran (@1.6.0)                       
##  local                               
##  cran (@0.4.1)                       
##  CRAN (R 3.4.3)                      
##  cran (@3.46.0)                      
##  CRAN (R 3.4.3)                      
##  CRAN (R 3.4.1)                      
##  CRAN (R 3.4.3)                      
##  CRAN (R 3.4.0)                      
##  cran (@0.18.0)                      
##  CRAN (R 3.4.3)                      
##  cran (@1.24.0)
> mdHeader("`utils::sessionInfo()`", level = 2)

utils::sessionInfo()

> sessionInfo()
## R version 3.4.3 (2017-11-30)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Sierra 10.12.6
## 
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] bindrcpp_0.2               forcats_0.2.0             
##  [3] stringr_1.2.0              dplyr_0.7.4               
##  [5] purrr_0.2.4                readr_1.1.1               
##  [7] tidyr_0.8.0                tibble_1.4.2              
##  [9] ggplot2_2.2.1              tidyverse_1.2.1           
## [11] knitr_1.19                 bcbioRNASeq_0.1.4         
## [13] DEGreport_1.14.1           quantreg_5.34             
## [15] SparseM_1.77               bcbioBase_0.0.3           
## [17] DESeq2_1.18.1              SummarizedExperiment_1.8.1
## [19] DelayedArray_0.4.1         matrixStats_0.53.0        
## [21] Biobase_2.38.0             GenomicRanges_1.30.1      
## [23] GenomeInfoDb_1.14.0        IRanges_2.12.0            
## [25] S4Vectors_0.16.0           BiocGenerics_0.24.0       
## 
## loaded via a namespace (and not attached):
##   [1] readxl_1.0.0                  backports_1.1.2              
##   [3] circlize_0.4.3                Hmisc_4.1-1                  
##   [5] AnnotationHub_2.10.1          plyr_1.8.4                   
##   [7] ConsensusClusterPlus_1.42.0   lazyeval_0.2.1               
##   [9] splines_3.4.3                 BiocParallel_1.12.0          
##  [11] digest_0.6.15                 BiocInstaller_1.28.0         
##  [13] ensembldb_2.2.0               htmltools_0.3.6              
##  [15] viridis_0.4.1                 magrittr_1.5                 
##  [17] checkmate_1.8.5               memoise_1.1.0                
##  [19] cluster_2.0.6                 limma_3.34.6                 
##  [21] ComplexHeatmap_1.17.1         Biostrings_2.46.0            
##  [23] annotate_1.56.1               Nozzle.R1_1.1-1              
##  [25] modelr_0.1.1                  R.utils_2.6.0                
##  [27] prettyunits_1.0.2             colorspace_1.3-2             
##  [29] rvest_0.3.2                   blob_1.1.0                   
##  [31] ggrepel_0.7.0                 haven_1.1.1                  
##  [33] crayon_1.3.4                  jsonlite_1.5                 
##  [35] tximport_1.6.0                RCurl_1.95-4.10              
##  [37] genefilter_1.60.0             bindr_0.1                    
##  [39] survival_2.41-3               glue_1.2.0                   
##  [41] gtable_0.2.0                  zlibbioc_1.24.0              
##  [43] XVector_0.18.0                MatrixModels_0.4-1           
##  [45] GetoptLong_0.1.6              shape_1.4.3                  
##  [47] scales_0.5.0                  vsn_3.46.0                   
##  [49] pheatmap_1.0.8                DBI_0.7                      
##  [51] edgeR_3.20.7                  Rcpp_0.12.15                 
##  [53] viridisLite_0.3.0             xtable_1.8-2                 
##  [55] progress_1.1.2                htmlTable_1.11.2             
##  [57] foreign_0.8-69                bit_1.1-12                   
##  [59] preprocessCore_1.40.0         Formula_1.2-2                
##  [61] htmlwidgets_1.0               httr_1.3.1                   
##  [63] RColorBrewer_1.1-2            acepack_1.4.1                
##  [65] reshape_0.8.7                 pkgconfig_2.0.1              
##  [67] XML_3.98-1.9                  R.methodsS3_1.7.1            
##  [69] nnet_7.3-12                   locfit_1.5-9.1               
##  [71] labeling_0.3                  reshape2_1.4.3               
##  [73] rlang_0.1.6                   AnnotationDbi_1.40.0         
##  [75] munsell_0.4.3                 cellranger_1.1.0             
##  [77] tools_3.4.3                   cli_1.0.0                    
##  [79] RSQLite_2.0                   devtools_1.13.4              
##  [81] broom_0.4.3                   evaluate_0.10.1              
##  [83] yaml_2.1.16                   bit64_0.9-7                  
##  [85] AnnotationFilter_1.2.0        nlme_3.1-131                 
##  [87] mime_0.5                      formatR_1.5                  
##  [89] R.oo_1.21.0                   grr_0.9.5                    
##  [91] xml2_1.2.0                    biomaRt_2.34.2               
##  [93] compiler_3.4.3                rstudioapi_0.7               
##  [95] curl_3.1                      interactiveDisplayBase_1.16.0
##  [97] affyio_1.48.0                 geneplotter_1.56.0           
##  [99] stringi_1.1.6                 highr_0.6                    
## [101] GenomicFeatures_1.30.2        lattice_0.20-35              
## [103] ProtGenerics_1.10.0           Matrix_1.2-12                
## [105] psych_1.7.8                   pillar_1.1.0                 
## [107] GlobalOptions_0.0.12          data.table_1.10.4-3          
## [109] cowplot_0.9.2                 bitops_1.0-6                 
## [111] Matrix.utils_0.9.6            httpuv_1.3.5                 
## [113] rtracklayer_1.38.3            affy_1.56.0                  
## [115] R6_2.2.2                      latticeExtra_0.6-28          
## [117] RMySQL_0.10.13                gridExtra_2.3                
## [119] codetools_0.2-15              assertthat_0.2.0             
## [121] rprojroot_1.3-2               rjson_0.2.15                 
## [123] withr_2.1.1                   mnormt_1.5-5                 
## [125] GenomicAlignments_1.14.1      Rsamtools_1.30.0             
## [127] GenomeInfoDbData_1.0.0        hms_0.4.1                    
## [129] grid_3.4.3                    rpart_4.1-12                 
## [131] rmarkdown_1.8                 dendsort_0.3.3               
## [133] logging_0.7-103               lubridate_1.7.1              
## [135] shiny_1.0.5                   base64enc_0.1-3              
## [137] basejump_0.2.0
> mdHeader("YAML params", level = 2)

YAML params

> print(params)
## $bcbFile
## [1] "data/bcb_sub.rda"
## 
## $design
## ~sampleclass
## <environment: 0x7fc00df2d708>
## 
## $contrast
## [1] "sampleclass"   "primary_tumor" "lung_notumor" 
## 
## $alpha
## [1] 0.05
## 
## $lfc
## [1] 1
## 
## $outputDir
## [1] "."

Benjamini, Yoav, and Yosef Hochberg. 1995. “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” J. R. Stat. Soc. Series B Stat. Methodol. 57 (1). [Royal Statistical Society, Wiley]: 289–300. http://www.jstor.org/stable/2346101.

Cui, Xiangqin, and Gary A Churchill. 2003. “Statistical Tests for Differential Expression in cDNA Microarray Experiments.” Genome Biol. 4 (4): 210. https://www.ncbi.nlm.nih.gov/pubmed/12702200.

Kolde, Raivo. 2015. Pheatmap: Pretty Heatmaps. https://CRAN.R-project.org/package=pheatmap.

Li, Wentian, Jan Freudenberg, Young Ju Suh, and Yaning Yang. 2014. “Using Volcano Plots and Regularized-Chi Statistics in Genetic Association Studies.” Comput. Biol. Chem. 48 (February): 77–83. doi:10.1016/j.compbiolchem.2013.02.003.

Love, Michael I, Wolfgang Huber, and Simon Anders. 2014. “Moderated Estimation of Fold Change and Dispersion for RNA-seq Data with DESeq2.” Genome Biol. 15 (12): 550. doi:10.1186/s13059-014-0550-8.

Patro, Rob, Geet Duggal, Michael I Love, Rafael A Irizarry, and Carl Kingsford. 2017. “Salmon Provides Fast and Bias-Aware Quantification of Transcript Expression.” Nat. Methods 14 (4): 417–19. doi:10.1038/nmeth.4197.

Soneson, Charlotte, Michael I Love, and Mark D Robinson. 2016. “Differential Analyses for RNA-seq: Transcript-Level Estimates Improve Gene-Level Inferences.” F1000Res. 4 (December). doi:10.12688/f1000research.7563.1.

Ward, Joe H, Jr. 1963. “Hierarchical Grouping to Optimize an Objective Function.” Journal of the American Statistical Association 58 (301). Taylor & Francis: 236–44. doi:10.1080/01621459.1963.10500845.

Wickham, Hadley. 2009. Ggplot2: Elegant Graphics for Data Analysis. Use R. Springer New York. doi:10.1007/978-0-387-98141-3.

Yang, Yee Hwa, Sandrine Dudoit, Percy Luu, David M Lin, Vivian Peng, John Ngai, and Terence P Speed. 2002. “Normalization for cDNA Microarray Data: A Robust Composite Method Addressing Single and Multiple Slide Systematic Variation.” Nucleic Acids Res. 30 (4): e15. https://www.ncbi.nlm.nih.gov/pubmed/11842121.